Microarray technology revolutionizes the ways by which genes and their functions are understood. Diverse applications of the microarray technology include profiling of gene expressions, classifying of tumors and cancers, cloning of genes, identifying of cancer genes, and discovering of therapeutic molecules.
Microarray technology employs microarray slides to assay hundreds and thousands of interested targets simultaneously. For the instance of gene expression profiling, a microarray slide contains arrays of immobilized DNA samples, often called spots. The spots are usually probed by two dye-tagged or radioactively labelled cDNAs. These cDNAs are made by reverse transcription of mRNAs from biological samples of interest, such as cells from patients, cells or organisms subjected to different stresses, or cells or organisms at varying developmental stages. Following the hybridisation step, the DNA arrays are scanned to generate an array image showing the fluorescence or radioactive intensity of each spot. The fluorescence or radioactive intensity is assumed to correlate with the expression level of each gene represented by a corresponding spot. Therefore, processing of an array image of a single slide can generate expression data of a large number of genes.
However, the task of translating an image of spots with varying intensities into a table linking intensity values to each gene has been impeded by microarray technology challenges. For example, the shape, size and location of spots fluctuate significantly across an array. These fluctuations may be caused by many factors such as printing, hybridisation, and slide-surface chemistry factors, and can significantly affect the interpretation of an array image. Actually, a small manufacturing defect in the same batch of arrays could affect the data analysis of specific genes.
For minimizing the printing variations, a known way is to have on-line inspection on printing quality and then, tune the printing parameters until the best print is obtained. U.S. Pat. No. 6,558,623. Unfortunately, even best printing- parameters are subjected to variations of print tips, environmental factors, and the like. The parameters need to be tuned from time to time. Moreover, for existing systems, best printing actually means that the printing parameters are within certain tolerance, so that there will be variations among spots, even for the best printed slides. In addition, random errors do occur, introducing extra variations into spots.
Another challenge for microarray technology is that it needs to process and analyse numerous images where a single image may contain thousands of spots. As discussed above, an image of a microarray slide may not be in its perfect orientation. Thus, prior to any correlation of two or more images, the images must have the same orientation, and spots on the images must have the same alignment. One way of alignment of arrays in one image is to use grids. Sophisticated machine vision algorithms have been implemented in various software packages to help selection of grids with high precision. In most cases, the user will identify the bounding area of a sub-grid by selecting comer spots. Kuklin, A. Using array image analysis to combat HTS bottlenecks, Genetic Eng. News, 19(19): 32 (1999). The number of columns and rows enclosed in the rectangle should match the expected number of rows and columns of spots in the array, which is known a priori.
The next step is to identify the location of the corner spots of the bounding sub-grids in the image. Then, the spot-finding algorithm uses that information to create the grid. It adjusts the location of the grid points and lines to locate the arrayed spots in the image. The software should allow for additional, quick manual adjustment of the grid points if the automatic spot finding method has not been identified certain spot positions.
After positioning of the grids and identifying of the spot location and size, the software will process both control and sample images. Image segmentation algorithms are used to appropriately identify and segregate the pixels associated with each spot signal area from its local background and possible other contaminations—even if the contamination has landed on the spot.
This approach involves human intervention in the process of spot location. The procedure becomes cumbersome when hundreds of microarray images need to be processed, as is the case in high-throughput screening. A single lab could produce from a few to thousands of array images per week. Any approach involving even the smallest human efforts per spot is certainly impractical at this scale.
Therefore, it is desirable to have an automated system that needs only input of the microarray configuration (e.g., number of rows and columns of spots) and a list of image files to process, after which analysis should be performed automatically. This system should be able to search the image for grid position, identify the layout of the array, localize the spots, and perform measurements without the need of user's intervention. The goals of complete automation in microarray image processing are to provide high accuracy in spot location, eliminate noise signals from the data analysis process, and minimize operator involvement in the procedure. This approach reduces time for personnel training and operator involvement. Automation ensures consistent, high quality control of data extraction.
However, microarray slides and fully automated microarray slide machine with vision inspection capability are expensive. The present invention provides a low cost and effective automated inspection system that can run on a personal computer for the quality control of microarray slides. In addition, the present invention enables a user to use microarray slides that are only partially well printed, so that more saving is provided. Other advantages of this invention will be apparent with reference to the detailed description.